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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deployment and Orchestration | 15% | - Environments (dev, staging, prod) - Jobs and scheduling in dbt Cloud - CI/CD with dbt Cloud - Git version control integration |
| Topic 2: Data Transformation Techniques | 25% | - Refactoring and incremental models - Jinja templating - Common table expressions and subqueries - Macros and packages |
| Topic 3: Models | 25% | - Seeds - Writing and managing SQL models - Snapshots - Sources and references - Materializations (table, view, ephemeral, incremental) |
| Topic 4: dbt Fundamentals | 15% | - dbt workflow and best practices - dbt Core vs dbt Cloud - dbt project structure |
| Topic 5: Testing and Documentation | 20% | - Schema tests (unique, not_null, accepted_values, relationships) - Custom data tests - dbt docs and DAG visualization - Documentation generation |
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NEW QUESTION # 156
You discover that an essential dbt macro behaves differently between your development environment and production due to a subtle database version discrepancy. Which debugging approach would be most efficient?
Answer: B,C,D
Explanation:
Each of these helps identify the root cause:A provides runtime insight into the issue. C lets you surface the relevant version for comparison. D allows for reproducible testing.
NEW QUESTION # 157
You've made several commits to your feature branch. Before merging into the main branch, you want to consolidate the changes into a single, clear commit. What Git technique would help achieve this?
Answer: D
Explanation:
Interactive rebasing (git rebase -i) provides fine-grained control to squash, edit, or rearrange commits, creating a cleaner commit history
NEW QUESTION # 158
Which of the following are essential elements of a well-structured pull request description?
Answer: B,C,D
Explanation:
A: Clear titles aid quick understanding of the PRs purpose. C: Explanations help reviewers and future developers understand the thought process behind the code. D: Issue references create traceability and context.
NEW QUESTION # 159
(Multiple Select)
Answer: B,D
Explanation:
Verifying source data and stepwise debugging are key. Tests help but come later. Changing the materialization is unlikely to directly address the null-value issue.
NEW QUESTION # 160
You are working on a complex dbt model with many Common Table Expressions (CTEs) and decide to move some of those CTEs into their own model to make your code more modular.
Is this a benefit of this approach?
The new model can be documented to explain its purpose and the logic it contains.
Answer: B
Explanation:
Yes, this is a benefit of breaking large CTE-heavy SQL models into modular dbt models. According to dbt and Analytics Engineering best practices, modularity improves clarity, maintainability, and documentation quality. When CTEs remain embedded inside a single large SQL file, their purposes are often unclear, difficult to document, and hard for other developers to reuse. By extracting a logical CTE into its own model, dbt treats it as a first-class resource-meaning it can have its own description, tests, documentation, lineage, and metadata defined in YAML.
dbt's documentation system allows each model to include a description explaining what the transformation does, the assumptions being made, and the expected behavior of the data. This aligns with the Analytics Engineering principle of creating self-documenting pipelines, where transformations are transparent and easier for downstream users to understand.
Additionally, modular models improve lineage visualization in the DAG. Instead of a single model hiding multiple transformation layers, a modular structure reveals how data flows through each intermediate step, helping both debugging and governance. Modularization also enables reusability-other models can reference the intermediate model rather than rebuilding the same logic through duplicated CTEs, supporting DRY (Don't Repeat Yourself) principles.
Therefore, moving CTEs into separate dbt models absolutely provides a documentation benefit and improves the overall engineering quality of the project.
NEW QUESTION # 161
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